TY - JOUR
T1 - Analysing the Moran effect and dispersal: their significance and interaction in synchronous population dynamics
AU - Ripa, Jörgen
PY - 2000
Y1 - 2000
N2 - Population synchrony over various geographical scales is known from a large number of taxa. Three main hypotheses have been put forward as explanations to this phenomenon. First, correlated environmental disturbances (so called Moran effect). Moran showed that at least for linear models, the population synchrony would exactly match that of the corresponding environment. Second, the migration, or dispersal, of individuals is liable to cause population synchrony. Third, nomadic predators have been proposed as a synchronising mechanism. In this paper, I analyse the first two explanations by linearizing a general population model with spatial structure. From this linear approximation I derive an expression for the population synchrony. The major results are: 1) Population synchrony can vary significantly depending on the timing of the population census. 2) The environmental correlation is always important. It sets the 'base level' of synchrony. 3) Dispersal is only an effective synchronising mechanism when the local dynamics are at least close to unstable. 4) These results are valid even in a model with delayed density dependence - with possibly cyclic dynamics. Time lag structure has little effect on synchrony. Some of the predictions presented here are supported by data from the literature.
AB - Population synchrony over various geographical scales is known from a large number of taxa. Three main hypotheses have been put forward as explanations to this phenomenon. First, correlated environmental disturbances (so called Moran effect). Moran showed that at least for linear models, the population synchrony would exactly match that of the corresponding environment. Second, the migration, or dispersal, of individuals is liable to cause population synchrony. Third, nomadic predators have been proposed as a synchronising mechanism. In this paper, I analyse the first two explanations by linearizing a general population model with spatial structure. From this linear approximation I derive an expression for the population synchrony. The major results are: 1) Population synchrony can vary significantly depending on the timing of the population census. 2) The environmental correlation is always important. It sets the 'base level' of synchrony. 3) Dispersal is only an effective synchronising mechanism when the local dynamics are at least close to unstable. 4) These results are valid even in a model with delayed density dependence - with possibly cyclic dynamics. Time lag structure has little effect on synchrony. Some of the predictions presented here are supported by data from the literature.
U2 - 10.1034/j.1600-0706.2000.890119.x
DO - 10.1034/j.1600-0706.2000.890119.x
M3 - Article
SN - 1600-0706
VL - 89
SP - 175
EP - 187
JO - Oikos
JF - Oikos
IS - 1
ER -